Motivation. We have created a modular project-based learning curriculum, Computer Science Frontiers (CSF) [1, 8], for secondary students in attempts to increase the persistence of computer science (CS) students in higher education. The CSF course is divided into four different modules (Distributed Computing, Internet of Things, Artificial Intelligence, and Software Engineering), each centered around a topic typically introduced to students only in higher education. Using the block-based programming environment NetsBlox [4], students are able to access various Application Programming Interfaces related to their interests [2, 3]. The goal of this course is to increase student interest in CS during high school - when first career choices occur [7] - in hopes they will persist in CS during their undergraduate studies.

While some high school courses like AP Computer Science Principles cover topics such as the internet, they do not typically include hands-on activities on the Internet of Things (IoT) and related concepts like distributed computing. Notable reasons include the complexity of programming distributed systems and the cost of supplying classrooms with the necessary hardware. However, most students already have access to a smartphone or other mobile device, which contains all the necessary hardware to introduce these topics through highly engaging interactive projects. We introduce an open-source mobile app called PhoneIoT that allows students to remotely access their devices and query live sensor data or manipulate the app's customizable interactive display. Unlike other tools such as App Inventor, PhoneIoT is not an app-creation tool; instead, we take a distributed approach, with students' code running on their computers and remotely accessing the phone. This makes it possible to have multiple student programs connect to a single device, or one program connect to multiple devices, creating a truly distributed application. To target broad audiences, PhoneIoT supports simple programming interfaces in both block-based form through NetsBlox (a networking-oriented fork of Snap!) and text form through PyBlox (a Python library). This demonstration will include the basic information needed to install and use PhoneIoT, an interactive walkthrough of a compelling NetsBlox project that students could complete on their first day using PhoneIoT, and finally some interesting projects implemented in Python.

Historically, female students have shown low interest in the field of computer science. Previous computer science curricula have failed to address the lack of female-centered computer science activities, such as socially relevant and real-life applications. Our new summer camp curriculum introduces the topics of artificial intelligence (AI), machine learning (ML) and other real-world subjects to engage high school girls in computing by connecting lessons to relevant and cutting edge technologies. Topics range from social media bots, sentiment of natural language in different media, and the role of AI in criminal justice, and focus on programming activities in the NetsBlox and Python programming languages. Summer camp teachers were prepared in a week-long pedagogy and peer-teaching centered professional development program where they concurrently learned and practiced teaching the curriculum to one another. Then, pairs of teachers led students in learning through hands-on AI and ML activities in a half-day, two-week summer camp. In this paper, we discuss the curriculum development and implementation, as well as survey feedback from both teachers and students.

The Computer Science Frontiers (CSF) project introduces teachers to the topics of artificial intelligence and distributed computing to engage their female students in computing by connecting lessons to relevant cutting edge technologies. Application topics include social media and news articles, as well as climate change, the arts (movies, music, and museum collections), and public health/medicine. CSF educators are prepared in a pedagogy and peer-teaching centered professional development program where they simultaneously learn and teach distributed computing, artificial intelligence, and internet of things lessons to each other. These professional developments allow educators to hone in on their teaching skills of these new topics and gain confidence in their ability to teach new computer science materials before running several activities with their students in the academic year classroom. In this workshop, teachers participating in the CS Frontiers professional development will give testimonials discussing their experiences teaching these topics in a two week summer camp. Attendees will then try out three computing activities, one from each Computer Science Frontiers module. Finally, there will be a question and answer session.

Providing learners with authentic interdisciplinary experiences is one strategy to foster positive perceptions of CS as a discipline that supports a breadth of applications. We designed a high school mini course using climate science as an interdisciplinary context, since it is of interest to today's youth and provides opportunities to engage with authentic applications of computing that leverage real data to examine issues. The course was designed to engage students in climate change, data visualization, distributed computing, and motivate the use of advanced data abstractions and practices. We used NetsBlox, an extension of Snap! that makes real datasets & Web services accessible through easy-to-use blocks. A paleoclimatologist led climate science discussions and research questions with students. Post-survey responses to questions probing insights students gained shed light on the positive impact on students' perceptions of CS through this interdisciplinary experience, and how it expanded their horizons for future STEM inquiry using data and computing. We share curricular details for use in high schools and takeaways to help promote rich programming experiences that improve students' perceptions of computing.

RoboScape Online is a robotics simulation platform designed to reduce the barriers to entry for teachers to use robots as an educational tool in their classrooms. With simulated robots in a shared virtual 3D environment, students can be provided with "hardware" at no cost, no maintenance required, and free for them to "take home" while simultaneously collaborating with peers anywhere in the world. The environment supports remote, hybrid and in- person learning equally well. Programming support is provided through NetsBlox, a block-based programming environment, creating a novice-friendly experience while providing strong distributed computing and collaboration tools. Existing curricula focused on computational thinking and cybersecurity in NetsBlox have been used with physical robots for several years. These activities have been recreated with virtual robots, and the exact same code works in the simulated environment as well. However, virtual robots and their environments aren't limited to only the same experiences as before. New robots can be designed equipped with sensors and actuators previously infeasible for classroom use, giving students more interesting "missions" to work on solutions to. The software is open source, with free servers available, and tools are also provided to create new scenarios and environments. Handouts explaining the basics of the RoboScape environment will be provided. A computer with a web browser is required to use the software.

Distributed computing, computer networking, and the Internet of Things are all around us, yet only computer science and engineering majors learn the technologies that enable our modern lives. This paper introduces PhoneIoT, a mobile app that makes it possible to teach some of the basic concepts of distributed computation and networked sensing to novices. PhoneIoT turns mobile phones and tablets into IoT devices and makes it possible to create highly engaging projects through NetsBlox, an open-source block-based programming environment focused on teaching distributed computing at the high school level. PhoneIoT lets NetsBlox programs-running in the browser on the student's computer—access available sensors. Since phones have touchscreens, PhoneIoT also allows building a GUI remotely from NetsBlox, which can be set to trigger custom code written by the student via NetsBlox's message system. The approach enables students to create quite advanced distributed projects, such as turning their phone into a game controller or tracking their exercise on top of an interactive Google Maps background with just a few blocks of code.

Distributed computing, computer networking, cyber-physical systems and the Internet of Things (IoT) are all around us, yet very little of the underlying concepts are being taught in introductory computer science courses in K12. Teaching IoT is especially problematic since it requires hardware with its own cost and maintenance requirements that many under-resourced schools cannot afford. However, smartphones are commonplace networked devices that boast a wide range of sensors. With the right approach, they can be used to introduce many advanced computing concepts even to novices. We have created a mobile app, PhoneIoT, which makes all available sensors accessible to the user's computer through NetsBlox, a block-based educational programming environment specializing in networking and distributed computing. Accessing PhoneIoT uses the same simple networking primitives as other NetsBlox services, so no additional knowledge is required. PhoneIoT makes it possible to collect sensor data, build a custom graphical interface on the device and receive corresponding events, all without leaving your browser running NetsBlox. This demonstration will showcase a few simple projects such as an exercise tracking app and how to turn your phone into a game controller.

While educational robotics and makerspaces are useful to modern STEM education, they introduce both physical and economic barriers to entry. By creating a simulated, networked environment, we can facilitate instruction on cyber-physical systems and related topics while reducing cost and complexity. The virtual environment created is connected to a block-based programming language, NetsBlox, to allow students to engage with the curriculum regardless of programming experience. The networked simulation and collaborative programming environment combine to become especially effective for distance learning. This demonstration showcases example scenarios providing students with a simple interface to interact with a simplified sensor network in a small area of a smart city and solve robot challenges.

The AP Computer Science Principles (CSP) high school course introduces students to computer science and programming. What should motivated students study after successful completion of AP CSP? The AP CSA class teaches Java programming and it has traditionally not attracted students from underrepresented groups. We are working on an alternative, projects-based course that will teach cutting edge CS concepts, such as distributed computing, computer networking, cybersecurity, the internet of things and machine learning, in a hands-on, accessible manner. Such an approach enables students to work on problems that interest them making computing more relevant and the curriculum more engaging. We utilize NetsBlox, a collaborative, block-based programming environment that extends Snap! with a few carefully selected abstractions that open up the vast array of resources freely available on the internet for student programs. Moreover, the tool enables students to work together on the same project remotely similarly to how Google Docs operate. This demonstration will introduce the environment and highlight its utility in creating distributed applications such as a shared whiteboard app and projects that access public domain scientific data sources and visualize them in various ways using online services such as Google Maps or charting. More information is available at https://netsblox.org.

Many block-based programming environments have proven to be effective at engaging novices in learning programming. However, most restrict access to the outside world, limiting learners to commands and computing resources built in to the environment. Some allow learners to drag and drop files, connect to sensors and robots locally or issue HTTP requests. But in a world where most of the applications in our daily lives are distributed (i.e., their functionality depends on communicating with other programs or accessing resources and data on the internet), the lack of support for beginners to envision and create such distributed programs is a lost opportunity. This paper argues that it is not only feasible, but crucial, to create environments with simple yet powerful abstractions that open up distributed computing and other widely used but advanced computing concepts including networking, the Internet of Things, and cybersecurity to novices. By thus removing the walls around our environments, we can expand opportunities for learning considerably: programs can access a wealth of online data and web services, and communicate with other projects. Moreover, these changes can enable young learners to collaborate with each other during program construction whether they share their physical location or study remotely. Importantly, providing access to the wider world will also help counter widespread student perceptions that block-based environments are mere toys, and show that they are capable of creating compelling applications. The paper presents NetsBlox, a programming environment that supports these ideas and shows that tools can be designed to democratize access to powerful ideas in computing.

Distance education has gained significance recently. However, its application to robotics education presents challenges as physical access to hardware is typically required. While educational robotics simulation platforms exist, most are limited in scope or do not facilitate remote collaboration well. This paper proposes a novel networked robotics simulator for education, where students are able to collaborate in a shared 3D virtual space their robots inhabit. The framework combines these simulated worlds with a block-based programming environment to enable distance robotics education for a wider audience. The users' programs run in a web browser on their computers and they issue commands to the virtual robots through a network protocol abstracted to simple-to-use blocks. Robots send back acknowledgements and sensor values through the same protocol. Interactive environments provide students with a more immersive educational experience, and allow for automatically evaluating student performance. In addition to sharing the virtual worlds remotely, students can also develop their programs together since the block-based environment supports both synchronous and asynchronous remote collaboration. The paper presents two scenarios showing a robotics challenge and an extension of the concept to a smart city scenario controlling traffic lights. Reduced barriers to entry for both robotics education and curriculum creation will allow for a more diverse set of students and course materials.

This paper presents a study using a robotics platform for teaching computing and cybersecurity concepts to high school students. 38 students attended a week-long camp, starting with projects such as a simulation-only game and a simple autonomous driving program for the robots in order to learn and apply computational thinking (CT) and networking skills. They were then assigned a series of challenges that required developing progressively more advanced cybersecurity measures to protect their robots. This culminated in a final challenge that required implementing defensive measures such as encryption, secure key exchange and sequence numbers. We used an evidence-centered design framework to construct rubrics for grading student work. The pre- and post-test results show that the interventions helped students learn cybersecurity and CT concepts, but they had difficulties with networking concepts. These results correlate with scores from the game and the final challenge. Overall, surveys show that the competition-based robotics learning framework engaged students and supported their overall learning, but our intervention needs to be modified to help students learn networking concepts

Wildlife poaching is a critical driver of biodiversity loss and population decline. Poaching is a particular threat to high value, large-bodied species, such as elephants, that are slow to reproduce. Increasingly, GPS tracking collars serve as a key tool for studying the behavior and monitoring wildlife globally, including application to anti-poaching efforts. However, collars provide indirect information on poaching, such as immobility, that is often not available in real time. In parallel to collar development, acoustic gunshot detection systems have proliferated in the military and law enforcement. Static systems in wildlife areas have been deployed for detecting poaching, but such systems do not scale geographically. This paper explores the idea of fusing GPS tracking collars with acoustic shockwave detectors to create an animal-borne anti-poaching sensor. A real-time alert of gunshots near elephant groups would enable rangers to respond immediately to such events. The two main technical challenges to such a system are battery life and detection accuracy. The paper presents a prototype designed for elephants that has great promise in addressing these significant technical challenges.

Poaching is one of the primary drivers of wildlife decline [1]. Animalborne sensors, particularly GPS-equipped collars, are used to enhance real-time wildlife protection. Innovations that can be integrated into these systems can immediately scale, offering broad application. GPS tracking data streams have been valuable to resolve a number of conservation challenges, but these systems have not been particularly effective in identifying poaching in real-time. Detecting poaching events is a critical need to provide actionable information for law enforcement.

C2STEM is a web-based learning environment founded on a novel paradigm that combines block-structured, visual programming with the concept of domain specific modeling languages (DSMLs) to promote the synergistic learning of discipline-specific and computational thinking (CT) concepts and practices. Our design-based, collaborative learning environment aims to provide students in K-12 classrooms with immersive experiences in CT through computational modeling in realistic scenarios (e.g., building models of scientific phenomena). The goal is to increase student engagement and include inclusive opportunities for developing key computational skills needed for the 21st century workforce. Research implementations that include a semester-long high school physics classroom study have demonstrated the effectiveness of our approach in supporting synergistic learning of STEM and CS/CT concepts and practices, especially when compared to a traditional classroom approach. This technology demonstration will showcase our CS+X (X = physics, marine biology, or earth science) learning environment and associated curricula. Participants can engage in our design process and learn how to develop curricular modules that cover STEM and CS/CT concepts and practices. Our work is supported by an NSF STEM+C grant and involves a multi-institutional team comprising Vanderbilt University, SRI International, Looking Glass Ventures, Stanford University, Salem State University, and ETR. More information, including example computational modeling tasks, can be found at C2STEM.org.

The paper presents RoboScape, a collaborative, networked robotics environment that makes key ideas in computer science accessible to groups of learners in informal learning spaces and K-12 classrooms. RoboScape is built on top of NetsBlox, an open-source, networked, visual programming environment based on Snap! that is specifically designed to introduce students to distributed computation and computer networking. RoboScape provides a twist on the state of the art of robotics learning platforms. First, a user's program controlling the robot runs in the browser and not on the robot. There is no need to download the program to the robot and hence, development and debugging become much easier. Second, the wireless communication between a student's program and the robot can be overheard by the programs of the other students. This makes cybersecurity an immediate need that students realize and can work to address. We have designed and delivered a cybersecurity summer camp to 24 students in grades between 7 and 12. The paper summarizes the technology behind RoboScape, the hands-on curriculum of the camp and the lessons learned.

NetsBlox (https://netsblox.org) is a web-based collaborative learning environment extending Snap! with a few carefully selected abstractions that enable students to create distributed applications. These include online multi-player games and client-server applications that access online STEAM data such as maps, weather, earthquakes, movie information, etc. The distributed computing abstractions supported by NetsBlox also make it possible to write robot control programs. These programs issue wireless commands to the robots which in turn reply with acknowledgements and sensor readings. The programs run in the browser making debugging much easier and eliminating the need to download anything to the robots. Furthermore, the environment makes it possible for other users to eavesdrop on the wireless messages and hijack the robots of others. This provides an excellent motivation for cybersecurity and makes it easy to teach it in a hands-on manner. We have created a cybersecurity curriculum for a week-long summer camp for high school students. Lessons include Denial of Service attacks, their detection and mitigation, cryptography, secure key exchange, replay attacks, etc. Each day student teams solve challenge problems that their robots have to carry out while under cyber-attack by the other participants. This technology demonstration is a companion to the paper "Teaching Cybersecurity with Networked Robots." It will provide the audience with a more practical view of this novel approach to hands-on cybersecurity education.

NetsBlox is a new collaborative learning environment extending Snap! with a few carefully selected abstractions that enable students to create distributed applications. In today's interconnected world, it will become increasingly important to have a basic understanding of computer networking and distributed computation yet these topics are rarely covered in K-12 curricula. Conversely, NetsBlox makes distributed programming accessible to beginner programmers using its simple yet powerful visual programming primitives, an intuitive user interface and a sophisticated cloud-based infrastructure. Moreover, the tool enables students to work together on the same project from different computers similarly to how Google Docs operate. This feature enables online collaboration and facilitates new ways to teach and learn programming. By allowing students to create multi-player games, NetsBlox provides increased motivation and is likely to prove engaging to students. By providing access to online public domain data sources, such as weather, earthquake, and air pollution data, in a unified manner, students will be able to create interesting science projects in a number of STEM fields promoting interdisciplinary learning. This technology demonstration will introduce the environment and demonstrate its utility in creating multi-player games, such as Battleship and Tic Tac Toe, as well as highlight two client-server applications that display weather and historical earthquake data, respectively, on top of an interactive Google Maps background. Audience members will be asked to participate in a massively parallel volunteer computing application doing prime factorization of large numbers. The open source public domain NetsBlox environment is accessible at http://netsblox.org.

Visual programming environments have been effective educational resources but are typically limited to a single user at a time. Given the amount of collaboration in modern software development and the value of group projects for beginner programmers, providing collaboration capabilities could be invaluable for students using a block-based programming environment. Online collaboration support would not only allow students to more actively work together on projects but would also facilitate other educational activities such as tutoring and interactive demos. Moreover, providing robust collaboration utilities allows the programming environment to more closely reflect the team-based nature of large scale, real-world programming projects. Note that collaborative editing offers a number of additional benefits under the hood: the same underlying software code can easily provide detailed logging of student actions and the capability to replay them. That is, researchers will be able to study how students solve problems and not just the end result. To this end, we have extended the Snap! visual programming environment to support real-time collaboration similar to Google Docs. In our model of collaboration, sprites and scripts can be edited by multiple users simultaneously, but the execution of the programs on the stage remains local. But is this the best collaboration model for students? If not, what alternative model would be better? Should the entire programming environment be synchronized across collaborators? Would simple screen sharing be more effective? Finally, how can we leverage a real-time collaborative environment to promote teamwork on programming projects?

This paper introduces NetsBlox, a visual programming environment for learning distributed programming principles. Extending both the visual formalism and open source code base of Snap!, NetsBlox provides two accessible distributed programming abstractions to simplify the process of creating networked applications: message passing and Remote Procedure Calls (RPC). Messaging passing allows NetsBlox applications to send data to other connected NetsBlox clients. Remote Procedure Calls enable seamless integration of third party services, such as Google Maps, weather, traffic and other public domain data sources, into NetsBlox applications. Other RPCs help coordinating distributed clients which may be difficult for novice programmers allowing the user to more quickly create captivating and sophisticated applications. These abstractions empower users to develop networked programs, including multi-player games and client-server applications. By providing networking support, NetsBlox not only allows users to learn distribute programming concepts but also makes programming more engaging by incorporating diverse services available on the web.

This paper describes the design and delivery of a highly successful MOOC that uses MATLAB to teach introductory computer programming to a wide audience. The decisions behind the curriculum and assessment strategy are detailed, and the results are evaluated based on three sessions of the course that saw 80,000 active students, two million lecture views and 100,000 auto-graded programming assignment sets during the three sessions delivered in 2015.

Minimally invasive robotic surgery techniques are becoming popular thanks to their enhanced patient benefits, including shorter recovery time, better cosmetic results and reduced discomforts. Less invasive procedures would be achieved with the use of Medical Capsule Robots (MCRs). These devices are characterized by low power requirements and small dimensions as well as uncompromising safety. MCRs operate wirelessly in abdominal Minimally Invasive Surgery (MIS) and Natural Orifice Transluminal Endoscopic Surgery (NOTES) or in the Gastrointestinal (GI) tract. The design process of MCRs, however, is expensive and time consuming. A platform for rapid prototyping MCRs is needed so that MCR researchers can reduce development costs and spend more time in studying innovative MCR applications. In this work, we introduce an open source modular platform geared toward rapid prototyping MCRs. To speed up the prototyping process, the MCR is programmed using TinyOS instead of bare-bone C. We present the hardware architecture of the platform, and the motivation for using TinyOS. To show the viability of TinyOS, we present results from an experiment involving sensing, actuation and wireless communication. This work lays the foundation for our future goal of building an integrated design environment for the design, analysis and simulation of MCRs.

The paper presents a visual, imperative language for the specification of constraints corresponding to domain-specific modeling languages (DSML) in the new WebGME modeling environment. The language is based on the visual notation introduced by Scratch. The novel feature of the approach is that the constraint language is just another DSML defined through UML class diagram-based metamodels. As such, it is easily extensible via metamodel inheritance. The visual constraint programs are automatically translated into asynchronous JavaScript code that utilizes the native WebGME APIs for evaluation.

For outdoor navigation, GPS provides the most widely-used means of node localization; however, the level of accuracy provided by low-cost receivers is typically insufficient for use in high-precision applications. Additionally, many of these applications do not require precise absolute Earth coordinates, but rather rely on relative positioning to infer information about the geometric configuration of the constituent nodes in a system. This paper presents a novel approach that uses GPS to derive relative location information for a scalable network of single-frequency receivers. Networked nodes share their raw satellite observations, enabling each node to localize its neighbors in a pairwise fashion as opposed to computing its own standalone position. Random and systematic errors are mitigated in novel ways, challenging long-standing beliefs that precision GPS systems require extensive stationary calibration times or complex equipment configurations. In addition to presenting the mathematical basis for our technique, a working prototype is developed, enabling experimental evaluation of several real-world test scenarios. The results of these experiments indicate sub-meter relative positioning accuracy under various conditions and in varying environments. This represents up to order of magnitude increase in precision over existing absolute positioning techniques or other unimodal GPS-based solutions.

One of the primary limitations to widescale adoption of many of today's high-precision location-aware applications is the cost associated with achieving the levels of accuracy necessary for safe and reliable operation. We have developed a relative localization solution enabling centimeter-scale positioning of a network of mobile nodes. The success of this research in the relative sense can be coupled with the known locations of a set of stationary nodes as well as the computational power of the cloud to open the door for an absolute localization service, whereby base stations at well-known locations can be used to transform relative coordinates into absolute locations. We will demonstrate the current progress of our work via live walking and driving experiments in which observers in an exhibition hall can issue commands to walkers and drivers in the field and see the results of these commands as determined by our positioning system in real-time.

Minimally invasive surgical techniques are becoming popular due to their enhanced patient benefits. Less invasive procedures can be achieved with the use of wireless Medical Capsule Robots (MCRs). MCRs are low powered and small in size and can be used for physiological parameter monitoring, therapy delivery, and biopsy sampling. Designing MCRs from the ground up is a costly and time consuming process. In this work, we present a flexible modular architecture to facilitate the design of MCRs and propose using TinyOS as the operating system. To assess the architecture and validate the feasibility of TinyOS, we implement a closed-loop control of a sensor-actuator system and compare the results with a traditional MCR built based on an 8051 microcontroller (MCU) programmed in plain C. Similar performances from the two approaches lead us to conclude that TinyOS is a valid option to implement a modular architecture for designing MCRs.

The paper presents a smartphone-based shooter localization system. As muzzle blasts are difficult to detect at longer distances and consequently present higher false detection rates, the system relies on shockwaves only. Each sensor uses four microphones to detect the Angle of Arrival and the length of the shockwave. This information, along with the sensor's own GPS coordinates, are shared among nearby smartphones. Assuming a known weapon type, it then proceeds to estimate the two possible projectile trajectory candidates for each sensor that are consistent with the observations in the horizontal plane of the sensors. A simple clustering algorithm identifies the correct projectile trajectory relying on as few as two sensors. The trajectory is then used to estimate the bearing to the shooter relative to each sensor. The paper presents the overall system architecture, the design of the sensor node that interfaces with the smartphone, the trajectory and bearing estimation algorithms, and the evaluation of the system based on a field experiment.

In many mobile wireless applications such as the automated driving of cars, formation flying of unmanned air vehicles, and source localization or target tracking with wireless sensor networks, it is more important to know the precise relative locations of nodes than their absolute coordinates. GPS, the most ubiquitous localization system available, generally provides only absolute coordinates. Furthermore, low-cost receivers can exhibit tens of meters of error or worse in challenging RF environments. This paper presents an approach that uses GPS to derive relative location information for multiple receivers. Nodes in a network share their raw satellite measurements and use this data to track the relative motions of neighboring nodes as opposed to computing their own absolute coordinates. The system has been implemented using a network of Android phones equipped with a custom Bluetooth headset and integrated GPS chip to provide raw measurement data. Our evaluation shows that centimeter-scale tracking accuracy at an update rate of 1 Hz is possible under various conditions with the presented technique. This is more than an order of magnitude more accurate than simply taking the difference of reported absolute node coordinates or other simplistic approaches due to the presence of uncorrelated measurement errors.

In many mobile wireless applications such as the automated driving of cars, formation flying of unmanned air vehicles, and source localization or target tracking with wireless sensor networks, it is more important to know the precise relative locations of nodes than their absolute coordinates. GPS, the most ubiquitous localization system available, generally provides only absolute coordinates. Furthermore, low-cost receivers can exhibit tens of meters of error or worse in challenging RF environments. This video demonstration presents an approach that uses GPS to derive relative location information for multiple receivers. Cooperating nodes in a network share their raw satellite measurements and use this data to track the relative motions of neighboring nodes as opposed to computing their own absolute coordinates. This is achieved via creation of a new error model that incorporates GPS localization errors specific to the multiple-receiver case, development of a new, highly accurate observation model that allows for the change in range between a single satellite and two receivers to be mapped through time, and the synthesis of these two models into a novel 3D pairwise tracking algorithm that uses the local GPS node as its own reference and requires only an approximate estimate of its own absolute position to achieve centimeter-scale relative tracking precision.

Molecular dynamics simulators are indispensable tools in the arsenal of chemical engineers and material scientists. However, they are often difficult to use and require programming skills as well as deep knowledge of both the given scientific domain and the simulation software itself. In this paper, we describe a metaprogramming approach where simulator experts can create a library of simulation components and templates of frequently used simulations. Domain experts, in turn, can build and customize their own simulations and the required input for the various supported simulators is automatically synthesized. The web-based environment also supports setting up a suite of simulation jobs, for example, to carry out automated parameter optimization, via a visual programming environment. The entire simulation setup – including the various parameters, the version of tools utilized and the results – is stored in a database to support searching and browsing of existing simulation outputs and facilitating the reproducibility of scientific results.

Over the past decade, wireless sensor networks have advanced in terms of hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been growing interest in mobile wireless sensor networks, and several small-profile sensing devices that are able to control their own movement have already been developed. Unfortunately, resource constraints inhibit the use of traditional navigation methods, because these typically require bulky, expensive, and sophisticated sensors, substantial memory and processor allocation, and a generous power supply. Therefore, alternative navigation techniques are required. In this paper we present TripNav, a localization and navigation system that is implemented entirely on resource-constrained wireless sensor nodes. Localization is realized using radio interferometric angle of arrival estimation, in which bearings to a mobile node from a small number of infrastructure nodes are estimated based on the observed phase differences of an RF interference signal. The position of the mobile node is then determined using triangulation. A digital compass is also employed to keep the mobile node from deviating from the desired trajectory. We demonstrate using a real-world implementation that a resource-constrained mobile sensor node can accurately perform waypoint navigation with an average position error of 0.95 m.

Acoustic shooter localization systems are being rapidly deployed in the field. However, these are standalone systems---either wearable or vehicle-mounted---that do not have networking capability even though the advantages of widely distributed sensing for locating shooters have been demonstrated before. The reason for this is that certain disadvantages of wireless network-based prototypes made them impractical for the military. The system that utilized stationary single-channel sensors required many sensor nodes, while the multi-channel wearable version needed to track the absolute self-orientation of the nodes continuously, a notoriously hard task. This paper presents an approach that overcomes the shortcomings of past approaches. Specifically, the technique requires as few as five single-channel wireless sensors to provide accurate shooter localization and projectile trajectory estimation. Caliber estimation and weapon classification are also supported. In addition, a single node alone can provide reliable miss distance and range estimates based on a single shot as long as a reasonable assumption holds. The main contribution of the work and the focus of this paper is the novel sensor fusion technique that works well with a limited number of observations. The technique is thoroughly evaluated using an extensive shot library.

Modern software-defined radios are large, expensive, and power-hungry devices and this, we argue, hampers their more widespread deployment and use, particularly in low-power, size-constrained application settings like mobile phones and sensor networks. To rectify this problem, we propose to put the software-defined radio on a diet by redesigning it around just two core chips -- an integrated RF transceiver and a Flash-based, mixed-signal FPGA. Modern transceivers integrate almost all RF front-end functions while emerging FPGAs integrate nearly all of required signal conditioning and processing functions. And, unlike conventional FPGAs, Flash-based FPGAs offer sleep mode power draws measured in the microamps and startup times measured in the microseconds, both of which are critical for low-power operation. If our platform architecture vision is realized, it will be possible to hold a software-defined radio in the palm of one's hand, build it for $100, and power it for days using the energy in a typical mobile phone battery. This will make software radios deployable in high densities and broadly accessible for research and education.

Mobile wireless sensors require position updates for tracking and navigation. We present a localization technique that uses the Doppler shift in radio transmission frequency observed by stationary sensors. We consider two scenarios. In the first, the mobile node is carried by a person. In the second, the mobile node controls a robot. In both approaches the mobile node transmits an RF signal, and infrastructure nodes measure the Doppler-shifted frequency. Such measurements enable us to calculate the position and velocity of the mobile transmitter. Our experimental results demonstrate that this technique is viable and accurate for resource-constrained mobile sensor tracking and navigation.

The paper describes a target tracking system running on a heterogeneous sensor network (HSN) and presents results gathered from a realistic deployment. The system fuses audio direction of arrival data from mote class devices and object detection measurements from embedded PCs equipped with cameras. The acoustic sensor nodes perform beamforming and measure the energy as a function of the angle. The camera nodes detect moving objects and estimate their angle. The sensor detections are sent to a centralized sensor fusion node via a combination of two wireless networks. The novelty of our system is the unique combination of target tracking methods customized for the application at hand and their implementation on an actual HSN platform.

The Mobile Air Quality Monitoring Network (MAQUMON) is presented. The system consists of a number of car-mounted sensor nodes measuring different pollutants in the air. The data points are tagged with location and time utilizing an on-board GPS. Periodically, the measurements are uploaded to a server, processed and then published on the SensorMap portal. Given a sufficient number of nodes and diverse mobility patterns, a detailed picture of the air quality in a large area will be obtained at a low cost.

Location-awareness is an important requirement for many mobile wireless applications today. When GPS is not applicable because of the required precision and/or the resource constraints on the hardware platform, radio interferometric ranging may offer an alternative. In this paper, we present a technique that enables the precise tracking of multiple wireless nodes simultaneously. It relies on multiple infrastructure nodes deployed at known locations measuring the position of tracked mobile nodes using radio interferometry. In addition to location information, the approach also provides node velocity estimates by measuring the Doppler shift of the interference signal. The performance of the technique is evaluated using a prototype implementation on mote-class wireless sensor nodes. Finally, a possible application scenario of dirty bomb detection in a football stadium is briefly described.

The paper presents a wireless sensor network-based mobilecountersniper system. A sensor node consists of a helmetmountedmicrophone array, a COTS MICAz mote for internodecommunication and a custom sensorboard that implementsthe acoustic detection and Time of Arrival (ToA) estimationalgorithms on an FPGA. A 3-axis compass providesself orientation and Bluetooth is used for communicationwith the soldier's PDA running the data fusion and the userinterface. The heterogeneous sensor fusion algorithm canwork with data from a single sensor or it can fuse ToA orAngle of Arrival (AoA) observations of muzzle blasts andballistic shockwaves from multiple sensors. The system estimatesthe trajectory, the range, the caliber and the weapontype. The paper presents the system design and the resultsfrom an independent evaluation at the US Army AberdeenTest Center. The system performance is characterized by 1-degree trajectory precision and over 95% caliber estimationaccuracy for all shots, and close to 100% weapon estimationaccuracy for 4 out of 6 guns tested.

In this paper, we address the problem of tracking cooperative mobile nodes in wireless sensor networks. Aiming at a resource efficient solution, we advocate the use of sensors that maintain their location information and rely on the tracking service only when their locations change. In the proposed approach, the tracked node transmits a signal and infrastructure nodes measure the Doppler shifts of the transmitted signal. We show that Mica2 motes can measure RF Doppler shifts with 0.2 Hz accuracy corresponding to a 0.14 m/s error in relative speed estimates using radio inter-ferometric technique.

This paper presents an enhanced version of a novel radio interferometric positioning technique for node localization in wireless sensor networks that provides both high accuracy and long range simultaneously. The ranging method utilizes two transmitters emitting radio signals at almost the same frequencies. The relative location is estimated by measuring the relative phase offset of the generated interference signal at two receivers. Here, we analyze how the selection of carrier frequencies affects the precision and maximum range. Furthermore, we describe how the interplay of RF multipath and ground reflections degrades the ranging accuracy. To address these problems, we introduce a technique that continuously refines the range estimates as it converges to the localization solution. Finally, we present the results of a field experiment where our prototype achieved 4~cm average localization accuracy for a quasi-random deployment of 16 COTS motes covering the area of two football fields. The maximum range measured was 170~m, four times the observed communication range. Consequently, node deployment density is no longer constrained by the localization technique, but rather by the communication range.

Experiences developing a sensor network-based acoustic shooter localization system are presented. The system is able to localize the position of a shooter and the trajectory of the projectile using observed acoustic events, such as the muzzle blast and the ballistic shockwave. The network consists of a large number of cheap sensors communicating through an ad-hoc wireless network, which enables the system to resolve multiple simultaneous acoustic sources, eliminate multipath effects, tolerate multiple sensor failures while providing good coverage and high accuracy, even in such challenging environment as urban terrain. The paper describes the hardware and software platform developed for this application and summarizes the lessons learned during the development of the system.

We present a mobile acoustic beacon based sensor node localization method. Our technique is passive in that the sensor nodes themselves do not need to generate an acoustic signal for ranging. This saves cost, power and provides stealthy operation. Furthermore, the beacon can generate much more acoustic energy than a severely resource constrained sensor node, thereby significantly increasing the range. The acoustic ranging method uses a linear frequency modulated signal that can be accurately detected by matched filtering. This provides longer range and higher accuracy than the current state-of-the-art. The localization algorithm was especially designed to work in such acoustically reverberant environment, as urban terrain. The algorithm presented handles non-Gaussian ranging errors caused by echoes. Node locations are computed centrally by solving a global non-linear optimization problem in an iterative and incremental fashion

We present a novel radio interference based sensor localization method for wireless sensor networks. The technique relies on a pair of nodes emitting radio waves simultaneously at slightly different frequencies. The carrier frequency of the composite signal is between the two frequencies, but has a very low frequency envelope. Neighboring nodes can measure the energy of the envelope signal as the signal strength. The relative phase offset of this signal measured at two receivers is a function of the distances between the four nodes involved and the carrier frequency. By making multiple measurements in an at least 8-node network, it is possible to reconstruct the relative location of the nodes in 3D. Our prototype implementation on the MICA2 platform yields an average localization error as small as 3 cm and a range of up to 160 meters. In addition to this high precision and long range, the other main advantage of the Radio Interferometric Positioning System (RIPS) is the fact that it does not require any sensors other than the radio used for wireless communication.

An ad-hoc wireless sensor network-based system is presented that detects and accurately locates shooters even in urban environments. The localization accuracy of the system in open terrain is competitive with that of existing centralized countersniper systems. However, the presented sensor network-based solution surpasses the traditional approach because it can mitigate acoustic multipath effects prevalent in urban areas and it can also resolve multiple simultaneous shots. These unique characteristics of the system are made possible by employing novel sensor fusion techniques that utilize the spatial and temporal diversity of multiple detections. In this article, in addition to the overall system architecture, the middleware services and the unique sensor fusion algorithms are described. An analysis of the experimental data gathered during field trials at US military facilities is also presented.

This paper presents an approach for dynamic software reconfiguration in sensor networks. Our approach utilizes explicit models of the design space of the embedded application. The design space is captured by formally modeling all the software components, their interfaces, and their composition. System requirements are expressed as formal constraints on QoS parameters that are measured at runtime. Reconfiguration is performed by transitioning from one point of the operation space to another based on the constraints. We demonstrate our approach using simulation results for a simple sensor network that performs one-dimensional tracking.

Wireless sensor network applications, similarly to other distributed systems, often require a scalable time synchronization service enabling data consistency and coordination. This paper describes the Flooding Time Synchronization Protocol (FTSP), especially tailored for applications requiring stringent precision on resource limited wireless platforms. The proposed time synchronization protocol uses low communication bandwidth and it is robust against node and link failures. The FTSP achieves its robustness by utilizing periodic flooding of synchronization messages, and implicit dynamic topology update. The unique high precision performance is reached by utilizing MAC-layer time-stamping and comprehensive error compensation including clock skew estimation. The sources of delays and uncertainties in message transmission are analyzed in detail and techniques are presented to mitigate their effects. The FTSP was implemented on the Berkeley Mica2 platform and evaluated in a 60-node, multi-hop setup. The average per-hop synchronization error was in the one microsecond range, which is markedly better than that of the existing RBS and TPSN algorithms.

An ad-hoc wireless sensor network-based system is presented that detects and accurately locates shooters even in urban environments. The system consists of a large number of cheap sensors communicating through an ad-hoc wireless network, thus it is capable of tolerating multiple sensor failures, provides good coverage and high accuracy, and is capable of overcoming multipath effects. The performance of the proposed system is superior to that of centralized countersniper systems in such challenging environment as dense urban terrain. In this paper, in addition to the overall system architecture, the acoustic signal detection, the most important middleware services and the unique sensor fusion algorithm are also presented. The system performance is analyzed using real measurement data obtained at a US Army MOUT (Military Operations in Urban Terrain) facility.

The paper describes a model-integrated approach for embedded software development that is based on domain-specific, multiple-view models used in all phases of the development process. Models explicitly represent the embedded software and the environment it operates in, and capture the requirements and the design of the application, simultaneously. Models are descriptive , in the sense that they allow the formal analysis, verification, and validation of the embedded system at design time. Models are also generative, in the sense that they carry enough information for automatically generating embedded systems using the techniques of program generators. Because of the widely varying nature of embedded systems, a single modeling language may not be suitable for all domains; thus, modeling languages are often domain-specific. To decrease the cost of defining and integrating domain-specific modeling languages and corresponding analysis and synthesis tools, the model-integrated approach is applied in a metamodeling architecture, where formal models of domain-specific modeling languages-called metamodels-play a key role in customizing and connecting components of tool chains. This paper discusses the principles and techniques of model-integrated embedded software development in detail, as well as the capabilities of the tools supporting the process. Examples in terms of real systems will be given that illustrate how the model-integrated approach addresses the physical nature, the assurance issues, and the dynamic structure of embedded software.

Model Integrated Computing (J. Sztipanovits and G. Karsai, 1997) involves defining a domain-specific language that allows for someone to effectively program an environment at whatever level the modeling environment creator deems appropriate. We have taken several complex, heterogeneous systems that posed problems of needing integration and requiring frequent reconfiguration at very high levels. The paper discusses: gathering the specifications for these systems, how the systems can be represented at these high levels in a paradigm also capturing the specifications, and then how reconfiguring this group can proceed. All of the activities constituting monitoring functionality and the resulting decision making exposed by the information system are shown to have been solved through utilization of Model Integrated Computing techniques.

Many large software systems are tightly integrated with their physical environments and must be adapted when their environment changes. Typically, software development methodologies do not place a great emphasis on modeling the system's environment, and hence environmental changes may lead to significant and complicated changes in the software. We argue that (1) the modeling of the environment should be an integral part of the process, and (2) to support software evolution, wherever possible, the software should be automatically generated. We present a model-integrated development approach that is capable of supporting cost effective system evolution in accordance with changes in the system's environment. The approach is supported by a "meta-architecture" that provides a framework for building model-based systems. This framework has been successfully used in various projects. One of these projects, a site-production flow visualization system for a large manufacturing operation, is analyzed in detail.

The design, implementation and deployment of computer applications tightly integrated with complex, changing environments is a difficult task. This paper presents the Multigraph Architecture (MGA) developed for building complex embedded systems. The MGA is a meta-level architecture which includes tools and methods to create domain specific model integrated program synthesis environments. These environments support the integrated modeling of systems independently from their implementation, include tools for model analysis and application specific model interpreters for the synthesis of executable programs.